import torch
import torch.nn.functional as F

from math import exp

from ignite.metrics import Metric

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import sync_all_reduce, reinit__is_reduced

class CalcSSIM(Metric):


    def __init__(self, output_transform=lambda x: x, device="cpu", thresh=[1, 2, 5], **kwargs):
        self.sum = 0
        self.count = 0
        self.avg = 0
        super(CalcSSIM, self).__init__(output_transform=output_transform, device=device)


    @reinit__is_reduced
    def reset(self):
        self.sum = 0
        self.count = 0
        self.avg = 0
        super().reset()


    @reinit__is_reduced
    def update(self, output):
        denoise, clean = output

        self.sum += calc_ssim(denoise, clean) * denoise.size(0)
        self.count += denoise.size(0)


    @sync_all_reduce("_num_examples", "_num_correct:SUM")
    def compute(self):
        if self.count == 0:
            raise NotComputableError("CalcSSIM has 0 sample!")

        self.avg = self.sum / self.count

        return self.avg

# https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
def gaussian(window_size, sigma) :
    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
    return gauss / gauss.sum()

def create_window(window_size, channel) :
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = torch.autograd.Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
    return window

def _ssim_map(img1, img2, window, window_size, channel) :
    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1 * mu2

    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
    sigma12   = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2

    C1 = 0.01 ** 2
    C2 = 0.03 ** 2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))

    return ssim_map

def calc_ssim(img1, img2, window_size = 11, size_average = True) :
    (_, channel, _, _) = img1.size()
    window = create_window(window_size, channel)

    if img1.is_cuda:
        window = window.cuda(img1.get_device())
    window = window.type_as(img1)

    ssim_map = _ssim_map(img1, img2, window, window_size, channel)

    if size_average:
        return ssim_map.mean()
    else:
        return ssim_map.mean(1).mean(1).mean(1)


if __name__ == "__main__":
    m = CMR()

    dis = torch.randint(6, (4, 10)).float()
    print(dis)

    for i in range(dis.size(0)):
        m.update((dis[i], None))
    
    res = m.compute()
    print(res)


